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<title>KEEL Suite 3.0 description</title>

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  <p><b>KEEL Suite 3.0 Description</b></p>

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<p>
KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks.
KEEL is developed to build and use different Data Mining models. The main features of KEEL are:</p>

<ul>

  <li> It includes around 100 data preprocessing algorithms proposed in the
specialized literature: data transformation, discretization, instance
and feature selection, noise filtering and so forth </li>

  <li> It contains a large collection of evolutionary algorithms for predicting
models, preprocessing methods (evolutionary feature and instance
selection among others) and postprocessing procedures (evolutionary
tuning of fuzzy rules). It also presents many state-of-the-art methods
for different areas of data mining such as decision trees, fuzzy rule
based systems or crisp rule learning.</li>

  <li> It has a statistical analysis library to analyze algorithms. </li>

  <li> It contains an user-friendly interface, oriented to the analysis of algorithms. 

  </li>

</ul>

<p>We can distinguish four parts in the graphic environment:</p>
 
<ul>
<li><b>Data Management:</b> the Data Management part allows users to create new Datasets or to create some partitions from an existing one. Also, you can view information from Datasets of your own, with the only restriction that they must meet the Keel format. In addition, it is possible to edit and apply transformations to the existing Datasets.<br>
Another important thing is that the Data Management part allows you to generate Datasets in the keel format from UCI files.<br><br></li>

<li><b>Experiments:</b> the Experiments part has the objective of designing the desired experiments 

  using a graphical interface. Doubtless, this is the more innovative tool integrated 

  in this tool. The objective is to use available datasets and algorithms to generate 

  a directory structure with all the necessary files needed to run the designed 

  experiments in the local computer selected by the user. Now, you can forget 

  scripts and other parameter files that made arduous the design of an experiment, 

  and begin to use the new windows based interface. <br>

With this program, yon only need to select the input data (datasets), the algorithms 

  you want to use and to make the opportune connections between them. Also it 

  is possible to concatenate methods, insert statistical tests, etc ...<br>

One of the tasks that was more simplified is probably the configuration of the 

  parameters; everything can be done from a simple dialog without requirement 

  of external configuration files.<br>

This part of KEEL has two main objectives: on the one hand, you can use 

  the software as a test and evaluation tool during the development of an algorithm. 

  On the other hand, it is also a good option in order to compare new developments 

  with standard algorithms already implemented and available in KEEL 2.0. <br><br></li>

<li><b>Educational:</b> the teaching part has the main objective of designing the desired experiments 

  using a graphical interface and an on-line execution of those experiments, being posible to stop and resume them as you need. Also, you can see the results of those experiments into the environment.<br>
However, this part has a reduced number of available methods and lacks of result method and statistical test<br><br></li>

<li><b>Modules:</b> This part includes new modules extending the functionalities of the KEEL software tool:
	<br/><br/>
	<ul>
		<li><b>Imbalanced learning:</b> A module specially designed for generating experiments with imbalanced
		data. </li>
		<br/>
		<li><b>Non-Parametric Statistical Analysis:</b> This module allows to easily analize the results of any experimental study, expresed in raw CSV format. To do so, it includes several well-known non-parametric statistical tests, ready to use.</li>

		<li><b>Semi-supervised Learning:</b> This module is devoted to the creation and design
of experiments related to semi-supervised learning.</li>

		<li><b>Multiple Instance Learning:</b> The multiple instance learning
module allows the user to create and prepare experiments for multi-instance Learning.</li>

	</ul>
</li>
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